How can data teams get the full benefit from event-driven architecture, without limiting themselves to generic Event Stream Processing (ESPs) like Confluent? Is there a way to avoid the complexity of building such a solution from scratch?

To support the implementation of event-driven architectures that are used in numerous real-world applications, event stream processing (ESP) is designed to process a continuous stream of device data and take action on it in real time. ESPs are usually embedded into a broader data stack and complement other solutions. 

ESP solutions generally offer the following capabilities: 

• Real-Time Processing: handles data as it arrives, enabling immediate analysis and action
• Scalability: supports large-scale data processing and scales horizontally to meet increasing demands
• Streaming Analytics: includes tools for predicting outcomes, detecting anomalies, and identifying trends on the streaming data itself
• Low Latency: ensures minimal delay between data input and actionable insights
• Interoperability: works seamlessly with existing systems and data sources
• Fault Tolerance: designed to be highly available and resilient, ensuring continuous operation even in the face of failures  

Benefits of Streaming Analytics include: 

• Real-time Insights: enables informed decisions in near real-time, as events occur
• Customized Customer Service: enables personalized responses based on live data analysis
• Operational efficiency and agility: streamlines processes and reduces time-to-action 
• Risk Management: quickly identifying and responding to fraud, security threats, and system failures

Increased Revenue: leverages timely insights to optimize business strategies and offerings can positively impact the bottom line

Let’s take a look at some of the more popular ESPs.

Confluent

Confluent Cloud is a fully managed Kafka service that enables the processing of streaming data in real-time. The Confluent Platform can be installed and run on premises in a free, open-source version, or in an enterprise-grade version of the product with more administrative, ops, and monitoring tools. Confluent simplifies connecting data sources to Kafka, building streaming applications, as well as securing, monitoring, and managing Kafka infrastructure. The platform often requires a stiff learning curve. 

Confluent offers a unified solution for real-time data pipelines, applications, and microservices, including comprehensive monitoring tools. It supports 100+ connectors, the MQTT proxy and a streaming database (ksqlDB). It is mostly used for pub-sub type integration patterns, where data producers publish messages to Kafka topics, and consumers subscribe to these topics to receive messages. This pattern decouples data producers from the data consumers, ensuring scalability and flexibility. 

Amazon Kinesis 

Amazon Kinesis, a managed service within AWS, offers real-time scalable solutions for processing and analyzing streaming data, allowing users to run SQL queries for real time insights. With this solution you can ingest real-time data such as video, audio, application logs, and other sources, and process and analyze the data as it arrives for ML, analytics, and other applications, enabling immediate responses. The pricing depends on the use cases and the level of usage. 

Apache Spark 

Apache Spark’s open-source, in-memory, distributed processing framework handles both batch and streaming workloads, primarily within a single framework. It is used most often for real time analysis for data science and engineering, and uses parallel in-memory computation. Spark has hundreds of connectors to the most common cloud databases and supports integration with common programming languages (R, Python, Scala, SQL). Its fault tolerant architecture ensures that data processing jobs can continue even if nodes fail. Spark can be complex to set up and the learning curve is usually quite steep. Issues with performance may arise when many joins are required. 

Cloudera 

Cloudera is used for data management, analytics, and machine learning. This enterprise data cloud solution handles real time streaming data, enabling ingestion, transformation and analysis of high volume data streams with big data storage management. It contains the major pre-installed big data environments (Hadoop, HBase, Mahout, PIG) and integrates with tools and frameworks including Apache Kafka, Apache Flink, and TensorFlow. Cloudera usually requires experienced data engineers for set up and management.

Rivery ETL

Rivery’s ETL, a SaaS offering provides transformation capabilities and performs in-database transformation. It supports running multiple scripts into a single data pipeline, automating data ingestion and transformation in a single data workflow. Rivery’s strength is in data extraction, offering a wide range of pre-built connectors to popular data sources and destinations, focusing mainly on cloud store sources and cloud store targets. Enterprises that have their data entirely on premises would need to connect to the cloud to be able to use Rivery’s services. 

When you need more than an ESP

ESPs such as Confluent are generic solutions designed specifically to implement stream-based integration patterns and architectures as part of a broader data stack, complementing other solutions. Since ESP are typically part of a wider solution, time to value will depend on integration flexibility, the skill set and expertise of your data team, as well as unexpected dependencies that often arise during complex IT projects.

But if your requirements are not run of the mill, you may need a more powerful solution. Smart DIH, a holistic, unified real-time data platform from GigaSpaces, is an event-based solution that consolidates data from diverse cloud and on-premises systems into a highly performant data layer. This unified layer exposes data access services to consuming applications and services. 

Instead of building a solution by integrating a number of components, Smart DIH offers out of the box capabilities. By decoupling data sources from digital services, Smart DIH protects underlying systems from overload, while exposing data as microservices in real-time through a low code interface to consuming applications. Smart DIH utilizes event-streaming, integration, in-memory computing, and low code data access microservices to enable numerous low latency data-driven use cases.

Smart DIH utilizes embedded event-based streaming as part of a broad range of capabilities. This event-driven platform is designed for high performance and high concurrency to support unlocking data from legacy and other diverse systems for data sharing, data accessibility and exposing data services to consuming apps and stakeholders.